Python - 将字典转换为嵌套字典

Question

我有一个字典:

{'Logistic Regression': u'                                precision    recall  f1-score   support\n\n              APAR Information       0.74      1.00      0.85       844\nAffected Products and Versions       0.00      0.00      0.00        18\n                        Answer       0.00      0.00      0.00        30\n   Applicable component levels       0.96      0.85      0.90       241\n             Error description       0.48      0.56      0.52       754\n                     Local fix       0.89      0.03      0.06       266\n                Modules/Macros       0.96      0.87      0.91       326\n                       Problem       0.00      0.00      0.00        63\n               Problem summary       0.51      0.73      0.60       721\n           Related information       0.00      0.00      0.00        22\n         Resolving The Problem       0.00      0.00      0.00        60\n                 Temporary fix       0.00      0.00      0.00        32\n                  circumvenion       0.00      0.00      0.00       124\n                     component       0.00      0.00      0.00        49\n                 temporary_fix       0.00      0.00      0.00         2\n\n                     micro avg       0.64      0.64      0.64      3552\n                     macro avg       0.30      0.27      0.26      3552\n                  weighted avg       0.60      0.64      0.58      3552\n'}

或

                                precision    recall  f1-score   support

              APAR Information       0.74      1.00      0.85       844
Affected Products and Versions       0.00      0.00      0.00        18
                        Answer       0.00      0.00      0.00        30
   Applicable component levels       0.96      0.85      0.90       241
             Error description       0.48      0.56      0.52       754
                     Local fix       0.89      0.03      0.06       266
                Modules/Macros       0.96      0.87      0.91       326
                       Problem       0.00      0.00      0.00        63
               Problem summary       0.51      0.73      0.60       721
           Related information       0.00      0.00      0.00        22
         Resolving The Problem       0.00      0.00      0.00        60
                 Temporary fix       0.00      0.00      0.00        32
                  circumvenion       0.00      0.00      0.00       124
                     component       0.00      0.00      0.00        49
                 temporary_fix       0.00      0.00      0.00         2

                     micro avg       0.64      0.64      0.64      3552
                     macro avg       0.30      0.27      0.26      3552
                  weighted avg       0.60      0.64      0.58      3552

我想把这个字典转换成一个嵌套的字典，比如，

{'Logistic Regression':
{'APAR Information':'0.74','1.00','0.85','844'},
{'Affected Products and Versions':'0.00','0.00','0.00','18'}
.
.
.}

如何实现？可以通过 dict 内置函数来完成吗？

Answer 1

这是一种方法。

演示:

d = {'Logistic Regression': u'                                precision    recall  f1-score   support\n\n              APAR Information       0.74      1.00      0.85       844\nAffected Products and Versions       0.00      0.00      0.00        18\n                        Answer       0.00      0.00      0.00        30\n   Applicable component levels       0.96      0.85      0.90       241\n             Error description       0.48      0.56      0.52       754\n                     Local fix       0.89      0.03      0.06       266\n                Modules/Macros       0.96      0.87      0.91       326\n                       Problem       0.00      0.00      0.00        63\n               Problem summary       0.51      0.73      0.60       721\n           Related information       0.00      0.00      0.00        22\n         Resolving The Problem       0.00      0.00      0.00        60\n                 Temporary fix       0.00      0.00      0.00        32\n                  circumvenion       0.00      0.00      0.00       124\n                     component       0.00      0.00      0.00        49\n                 temporary_fix       0.00      0.00      0.00         2\n\n                     micro avg       0.64      0.64      0.64      3552\n                     macro avg       0.30      0.27      0.26      3552\n                  weighted avg       0.60      0.64      0.58      3552\n'}
result = {}
for i, v in enumerate(d["Logistic Regression"].splitlines()):
    if i == 0:
        continue
    val = v.strip().split("       ")
    if val[0]:
        result[val[0]] = " ".join(val[1:]).split()

for k, v in result.items():
    print(k)
    print(v)

输出:

weighted avg
[u'0.60', u'0.64', u'0.58', u'3552']
Local fix
[u'0.89', u'0.03', u'0.06', u'266']
Affected Products and Versions
[u'0.00', u'0.00', u'0.00', u'18']
component
[u'0.00', u'0.00', u'0.00', u'49']
Resolving The Problem
[u'0.00', u'0.00', u'0.00', u'60']
Error description
[u'0.48', u'0.56', u'0.52', u'754']
Problem summary
[u'0.51', u'0.73', u'0.60', u'721']
macro avg
[u'0.30', u'0.27', u'0.26', u'3552']
Related information
[u'0.00', u'0.00', u'0.00', u'22']
Applicable component levels
[u'0.96', u'0.85', u'0.90', u'241']
micro avg
[u'0.64', u'0.64', u'0.64', u'3552']
Answer
[u'0.00', u'0.00', u'0.00', u'30']
APAR Information
[u'0.74', u'1.00', u'0.85', u'844']
Problem
[u'0.00', u'0.00', u'0.00', u'63']
Modules/Macros
[u'0.96', u'0.87', u'0.91', u'326']
temporary_fix
[u'0.00', u'0.00', u'0.00', u'2']
circumvenion
[u'0.00', u'0.00', u'0.00', u'124']
Temporary fix
[u'0.00', u'0.00', u'0.00', u'32']